1. Field of Art
The present disclosure relates to the field of digital video and audio, and more specifically, to training and using machine learned classifiers for assessing the quality of content in videos.
2. Background
As video hosting services such as YOUTUBE™ have gained popularity, they have become an important new entertainment platform. Viewers go to video hosting websites to enjoy a variety of content, such as musical performance, stand-up comedy routines, technical demonstrations and instructions, educational presentations, and many other types of content.
The process for discovering high quality content on video hosting websites is sub-optimal for both viewers and content owners seeking an audience. There are currently millions of videos uploaded to video hosting websites, and the inventory of content grows daily. In this crowded space it is difficult for content owners to establish a presence, even if the content they post is of high quality. In order for high quality content to be noticed by the public it must be differentiated from the innumerable other videos that are uploaded. While content owners can tag videos with text descriptors such as “music,” “comedy,” and “prank,” this type of tagging is not sufficient because it simply identifies the category of video, but does not distinguish between good content and bad content. Where content providers do tag videos with qualitative tags—e.g., “awesome,” “amazing content,” or “funniest ever”—these tags are typically self-serving, and cannot be trusted as accurately representing the quality of the content in the video.
Since most video hosting services promote the videos with the most views, a content owner with high-quality content can sometimes distinguish her videos from others in a crowded category just by gaining a large viewership. Viewership, however, is not perfectly correlated with content quality. The number of viewers that watch a given video is often a function of the number of existing viewers that the video already has; users tend to watch the most popular videos because these popular videos tend to be included in lists of suggested videos, primarily because of their view counts. This creates a feedback loop where the popular videos become even more popular, even if they are not necessarily the highest quality content available.